In [1]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
In [3]:
data=pd.read_csv('C:/Users/Rakesh/Datasets/flipkart_reviews.csv')
In [4]:
data.head()
Out[4]:
Product_name Review Rating
0 Lenovo Ideapad Gaming 3 Ryzen 5 Hexa Core 5600... Best under 60k Great performanceI got it for a... 5
1 Lenovo Ideapad Gaming 3 Ryzen 5 Hexa Core 5600... Good perfomence... 5
2 Lenovo Ideapad Gaming 3 Ryzen 5 Hexa Core 5600... Great performance but usually it has also that... 5
3 DELL Inspiron Athlon Dual Core 3050U - (4 GB/2... My wife is so happy and best product 👌🏻😘 5
4 DELL Inspiron Athlon Dual Core 3050U - (4 GB/2... Light weight laptop with new amazing features,... 5
In [5]:
data.isnull().sum()
Out[5]:
Product_name    0
Review          0
Rating          0
dtype: int64
In [6]:
import nltk
import re
nltk.download('stopwords')
stemmer=nltk.SnowballStemmer('english')
from nltk.corpus import stopwords
import string
stopword=set(stopwords.words('english'))

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\Rakesh\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
In [7]:
def clean(text):
    text = str(text).lower()
    text = re.sub('\[.*?\]', '', text)
    text = re.sub('https?://\S+|www\.\S+', '', text)
    text = re.sub('<.*?>+', '', text)
    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
    text = re.sub('\n', '', text)
    text = re.sub('\w*\d\w*', '', text)
    text = [word for word in text.split(' ') if word not in stopword]
    text=" ".join(text)
    text = [stemmer.stem(word) for word in text.split(' ')]
    text=" ".join(text)
    return text
In [8]:
data['Review']=data['Review'].apply(clean)

Sentiment Analysis of Flipkart reviews¶

In [9]:
ratings=data['Rating'].value_counts()
In [10]:
numbers= ratings.index
quantity=ratings.values
In [11]:
import plotly.express as px
figure=px.pie(data,values=quantity,names=numbers,hole=0.5)
figure.show()

60% of the reviews are 5/5 ratings to the product they buy at flipkart¶

In [12]:
text=" ".join(i for i in data.Review)
stopwords=set(STOPWORDS)
wordcloud=WordCloud(stopwords=stopwords,background_color='white').generate(text)
plt.figure(figsize=(15,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

Analyzing the sentiment by adding three columns¶

In [16]:
nltk.download('vader_lexicon')
sentiments=SentimentIntensityAnalyzer()
data['Positive']=[sentiments.polarity_scores(i)['pos'] for i in data['Review']]
data['Negative']=[sentiments.polarity_scores(i)['neg'] for i in data['Review']]
data['Neutral']=[sentiments.polarity_scores(i)['neu'] for i in data['Review']]
data=data[["Review","Positive","Negative","Neutral"]]
print(data.head())

[nltk_data] Downloading package vader_lexicon to
[nltk_data]     C:\Users\Rakesh\AppData\Roaming\nltk_data...
[nltk_data]   Package vader_lexicon is already up-to-date!

                                              Review  Positive  Negative  \
0  best  great performancei got around  backup bi...     0.395     0.101   
1                                        good perfom     0.744     0.000   
2  great perform usual also game laptop issu batt...     0.277     0.000   
3                        wife happi best product 👌🏻😘     0.512     0.000   
4  light weight laptop new amaz featur batteri li...     0.000     0.000   

   Neutral  
0    0.504  
1    0.256  
2    0.723  
3    0.488  
4    1.000
In [18]:
#Now lets see how most of the reviewers think about the product and services of flipkart
x=sum(data['Positive'])
y=sum(data['Negative'])
z=sum(data['Neutral'])
In [19]:
def sentiment_score(a,b,c):
    if (a>b) and (a>c):
        print('Positive😊')
    if (b>a) and (b>c):
        print('Negative😠')
    else:
        print('Neutral🙂')
In [20]:
sentiment_score(x,y,z)

Neutral🙂
In [21]:
print("Positive: ", x)
print("Negative: ", y)
print("Neutral: ", z)

Positive:  923.5529999999985
Negative:  96.77500000000013
Neutral:  1283.6880000000006